Volume XLII-2/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 543-549, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-543-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 543-549, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W3-543-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Feb 2017

23 Feb 2017

IMPROVING GOOGLE'S CARTOGRAPHER 3D MAPPING BY CONTINUOUS-TIME SLAM

A. Nüchter1,2, M. Bleier2, J. Schauer1,2, and P. Janotta3 A. Nüchter et al.
  • 1Informatics VII – Robotics and Telematics, Julius Maximilian University of Würzburg, Germany
  • 2Zentrum für Telematik e.V., Würzburg, Germany
  • 3Measurement in Motion GmbH, Theilheim, Germany

Keywords: SLAM, trajectory optimization, backpack, personal laser scanner, 3D point clouds

Abstract. This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google’s results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.